AI 中文总结
研究提出基于输入数据对称性的量子神经网络正则化新方法，通过引入惩罚项，提升训练速度与泛化能力，经实验验证有效并给出理论泛化界。
AI 中文摘要
利用数据对称性近来成为量子神经网络（QNNs）提升泛化和训练效率的关键策略。本研究提出基于输入数据对称性的QNNs正则化新方法。通过引入鼓励模型与数据对称性对齐的惩罚项，提升训练速度和泛化能力。此基于对称性的正则化易于实现，无需对称群先验知识，通过实验验证有效并给出理论泛化界。
英文摘要
Leveraging data symmetries has recently become a key strategy in quantum neural networks (QNNs) to improve training efficiency. In this study, we propose a symmetry-informed regularization method for QNNs based on an input density matrix. By introducing a penalty term that encourages the model to align with data symmetry, our method enables improved training speed. This symmetry-based regularization is simple to implement and does not require an explicitly specified symmetry group, although it requires access to training samples or to the input distribution from which an empirical or theoretical input density matrix can be constructed. We evaluate the method through numerical experiments on both classification tasks and quantum generative adversarial networks. In the small-scale classification experiments, the regularizer produced modest improvements in early-stage convergence and test loss. Our findings highlight the potential of symmetry-aware regularization in enhancing the performance of QML models.
Commentsv2: Major corrections with significant updates
